When at least one microphone signal is available from each side of the head it is possible to optimally combine the microphone outputs to produce a super-directional response. Most well known binaural directional processors achieving a directional response are based on broadside array configurations, adaptive Least Minimum Square (LMS) or more sophisticated Blind Source Separation (BSS) strategies.
Broadside array configurations produce efficient directional responses when the wavelength of the sound sources is relatively larger than the spacing between microphones. As a result broadside array techniques are only effective for the low-frequency component of sounds when used in binaural array configurations.
Unlike broadside array designs Least Minimum Square (LMS) systems efficiently produce directionality independently of frequency or spacing between microphones. In such systems Voice Active Detectors (VAD) are needed to capture a desired signal during times where the ratio between signal level and noise level is relatively large. This captured desired signal, typically referred to as the estimated desired signal is compared to filtered outputs from the microphones, thus producing an estimated error signal. The objective of the LMS is to minimize the square of the estimated error signal by iteratively improving the filter weights applied to the microphone output signals. However, the estimated desired signal may not entirely reflect the real desired signal, and therefore the adaptation of the filter weights may not always minimize the true error of the system. The optimization largely depends on the efficiency of the VAD employed. Unfortunately, most VADs work well in relatively high signal-to-noise ratio environments but their performance significantly degrades as the signal-to-noise ratio decreases.
Blind Source Separation (BSS) schemes operate by efficiently computing a set of phase cancelling filters producing directional responses in all spatial locations where sound sources are present. As a result, the system produces as many outputs as there are sound sources present without specifically targeting a desired sound source. BSS schemes also require post-filtering algorithms in order to select an output with a desired target signal. The problems with BSS approaches are; the excessive computational overload required for efficiently computing phase cancelling filters, dependence of the filters on reverberation and on small movements of the source or listener, and the identification of the one output related to the target signal, which in most cases is unknown and the prior identification of the number of sound sources present in the environment to guarantee separation between sound sources.
There remains a need to provide improved or alternative methods and systems for producing directional output signals.